Overview

Dataset statistics

Number of variables15
Number of observations506
Missing cells5
Missing cells (%)0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory59.4 KiB
Average record size in memory120.3 B

Variable types

Numeric14
Categorical1

Alerts

Unnamed: 0 is highly correlated with RAD and 1 other fieldsHigh correlation
CRIM is highly correlated with ZN and 8 other fieldsHigh correlation
ZN is highly correlated with CRIM and 4 other fieldsHigh correlation
INDUS is highly correlated with CRIM and 7 other fieldsHigh correlation
NOX is highly correlated with CRIM and 8 other fieldsHigh correlation
RM is highly correlated with LSTAT and 1 other fieldsHigh correlation
AGE is highly correlated with CRIM and 7 other fieldsHigh correlation
DIS is highly correlated with CRIM and 6 other fieldsHigh correlation
RAD is highly correlated with Unnamed: 0 and 3 other fieldsHigh correlation
TAX is highly correlated with Unnamed: 0 and 8 other fieldsHigh correlation
PTRATIO is highly correlated with MEDVHigh correlation
LSTAT is highly correlated with CRIM and 7 other fieldsHigh correlation
MEDV is highly correlated with CRIM and 7 other fieldsHigh correlation
Unnamed: 0 is highly correlated with RAD and 1 other fieldsHigh correlation
CRIM is highly correlated with RAD and 1 other fieldsHigh correlation
ZN is highly correlated with INDUS and 3 other fieldsHigh correlation
INDUS is highly correlated with ZN and 6 other fieldsHigh correlation
NOX is highly correlated with ZN and 6 other fieldsHigh correlation
RM is highly correlated with LSTAT and 1 other fieldsHigh correlation
AGE is highly correlated with ZN and 5 other fieldsHigh correlation
DIS is highly correlated with ZN and 4 other fieldsHigh correlation
RAD is highly correlated with Unnamed: 0 and 4 other fieldsHigh correlation
TAX is highly correlated with Unnamed: 0 and 7 other fieldsHigh correlation
PTRATIO is highly correlated with MEDVHigh correlation
LSTAT is highly correlated with INDUS and 5 other fieldsHigh correlation
MEDV is highly correlated with RM and 2 other fieldsHigh correlation
CRIM is highly correlated with INDUS and 4 other fieldsHigh correlation
ZN is highly correlated with INDUS and 1 other fieldsHigh correlation
INDUS is highly correlated with CRIM and 3 other fieldsHigh correlation
NOX is highly correlated with CRIM and 4 other fieldsHigh correlation
AGE is highly correlated with NOX and 1 other fieldsHigh correlation
DIS is highly correlated with CRIM and 3 other fieldsHigh correlation
RAD is highly correlated with CRIM and 1 other fieldsHigh correlation
TAX is highly correlated with CRIM and 1 other fieldsHigh correlation
LSTAT is highly correlated with MEDVHigh correlation
MEDV is highly correlated with LSTATHigh correlation
Unnamed: 0 is highly correlated with ZN and 11 other fieldsHigh correlation
CRIM is highly correlated with INDUS and 2 other fieldsHigh correlation
ZN is highly correlated with Unnamed: 0 and 8 other fieldsHigh correlation
INDUS is highly correlated with Unnamed: 0 and 9 other fieldsHigh correlation
NOX is highly correlated with Unnamed: 0 and 10 other fieldsHigh correlation
RM is highly correlated with Unnamed: 0 and 4 other fieldsHigh correlation
AGE is highly correlated with Unnamed: 0 and 8 other fieldsHigh correlation
DIS is highly correlated with Unnamed: 0 and 9 other fieldsHigh correlation
RAD is highly correlated with Unnamed: 0 and 9 other fieldsHigh correlation
TAX is highly correlated with Unnamed: 0 and 6 other fieldsHigh correlation
PTRATIO is highly correlated with Unnamed: 0 and 10 other fieldsHigh correlation
B is highly correlated with Unnamed: 0 and 2 other fieldsHigh correlation
LSTAT is highly correlated with Unnamed: 0 and 7 other fieldsHigh correlation
MEDV is highly correlated with Unnamed: 0 and 9 other fieldsHigh correlation
Unnamed: 0 is uniformly distributed Uniform
Unnamed: 0 has unique values Unique
ZN has 372 (73.5%) zeros Zeros

Reproduction

Analysis started2023-04-10 15:54:43.844039
Analysis finished2023-04-10 15:55:41.306704
Duration57.46 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

Unnamed: 0
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct506
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean252.5
Minimum0
Maximum505
Zeros1
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2023-04-10T15:55:41.502156image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile25.25
Q1126.25
median252.5
Q3378.75
95-th percentile479.75
Maximum505
Range505
Interquartile range (IQR)252.5

Descriptive statistics

Standard deviation146.2138844
Coefficient of variation (CV)0.5790648888
Kurtosis-1.2
Mean252.5
Median Absolute Deviation (MAD)126.5
Skewness0
Sum127765
Variance21378.5
MonotonicityStrictly increasing
2023-04-10T15:55:41.798068image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01
 
0.2%
3321
 
0.2%
3451
 
0.2%
3441
 
0.2%
3431
 
0.2%
3421
 
0.2%
3411
 
0.2%
3401
 
0.2%
3391
 
0.2%
3381
 
0.2%
Other values (496)496
98.0%
ValueCountFrequency (%)
01
0.2%
11
0.2%
21
0.2%
31
0.2%
41
0.2%
51
0.2%
61
0.2%
71
0.2%
81
0.2%
91
0.2%
ValueCountFrequency (%)
5051
0.2%
5041
0.2%
5031
0.2%
5021
0.2%
5011
0.2%
5001
0.2%
4991
0.2%
4981
0.2%
4971
0.2%
4961
0.2%

CRIM
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct504
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.613523557
Minimum0.00632
Maximum88.9762
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2023-04-10T15:55:42.112598image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.00632
5-th percentile0.02791
Q10.082045
median0.25651
Q33.6770825
95-th percentile15.78915
Maximum88.9762
Range88.96988
Interquartile range (IQR)3.5950375

Descriptive statistics

Standard deviation8.601545105
Coefficient of variation (CV)2.380376098
Kurtosis37.13050913
Mean3.613523557
Median Absolute Deviation (MAD)0.22145
Skewness5.223148798
Sum1828.44292
Variance73.9865782
MonotonicityNot monotonic
2023-04-10T15:55:42.418284image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.015012
 
0.4%
14.33372
 
0.4%
0.034661
 
0.2%
0.031131
 
0.2%
0.030491
 
0.2%
0.025431
 
0.2%
0.024981
 
0.2%
0.013011
 
0.2%
0.061511
 
0.2%
0.054971
 
0.2%
Other values (494)494
97.6%
ValueCountFrequency (%)
0.006321
0.2%
0.009061
0.2%
0.010961
0.2%
0.013011
0.2%
0.013111
0.2%
0.01361
0.2%
0.013811
0.2%
0.014321
0.2%
0.014391
0.2%
0.015012
0.4%
ValueCountFrequency (%)
88.97621
0.2%
73.53411
0.2%
67.92081
0.2%
51.13581
0.2%
45.74611
0.2%
41.52921
0.2%
38.35181
0.2%
37.66191
0.2%
28.65581
0.2%
25.94061
0.2%

ZN
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct26
Distinct (%)5.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.36363636
Minimum0
Maximum100
Zeros372
Zeros (%)73.5%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2023-04-10T15:55:42.703031image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q312.5
95-th percentile80
Maximum100
Range100
Interquartile range (IQR)12.5

Descriptive statistics

Standard deviation23.32245299
Coefficient of variation (CV)2.052375864
Kurtosis4.031510084
Mean11.36363636
Median Absolute Deviation (MAD)0
Skewness2.225666323
Sum5750
Variance543.9368137
MonotonicityNot monotonic
2023-04-10T15:55:42.968130image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
0372
73.5%
2021
 
4.2%
8015
 
3.0%
2210
 
2.0%
12.510
 
2.0%
2510
 
2.0%
407
 
1.4%
456
 
1.2%
306
 
1.2%
905
 
1.0%
Other values (16)44
 
8.7%
ValueCountFrequency (%)
0372
73.5%
12.510
 
2.0%
17.51
 
0.2%
181
 
0.2%
2021
 
4.2%
214
 
0.8%
2210
 
2.0%
2510
 
2.0%
283
 
0.6%
306
 
1.2%
ValueCountFrequency (%)
1001
 
0.2%
954
 
0.8%
905
 
1.0%
852
 
0.4%
82.52
 
0.4%
8015
3.0%
753
 
0.6%
703
 
0.6%
604
 
0.8%
553
 
0.6%

INDUS
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct76
Distinct (%)15.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.13677866
Minimum0.46
Maximum27.74
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2023-04-10T15:55:43.248031image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.46
5-th percentile2.18
Q15.19
median9.69
Q318.1
95-th percentile21.89
Maximum27.74
Range27.28
Interquartile range (IQR)12.91

Descriptive statistics

Standard deviation6.860352941
Coefficient of variation (CV)0.6160087358
Kurtosis-1.233539601
Mean11.13677866
Median Absolute Deviation (MAD)6.32
Skewness0.2950215679
Sum5635.21
Variance47.06444247
MonotonicityNot monotonic
2023-04-10T15:55:43.531402image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18.1132
26.1%
19.5830
 
5.9%
8.1422
 
4.3%
6.218
 
3.6%
21.8915
 
3.0%
3.9712
 
2.4%
9.912
 
2.4%
8.5611
 
2.2%
10.5911
 
2.2%
5.8610
 
2.0%
Other values (66)233
46.0%
ValueCountFrequency (%)
0.461
 
0.2%
0.741
 
0.2%
1.211
 
0.2%
1.221
 
0.2%
1.252
0.4%
1.321
 
0.2%
1.381
 
0.2%
1.472
0.4%
1.524
0.8%
1.692
0.4%
ValueCountFrequency (%)
27.745
 
1.0%
25.657
 
1.4%
21.8915
 
3.0%
19.5830
 
5.9%
18.1132
26.1%
15.043
 
0.6%
13.925
 
1.0%
13.894
 
0.8%
12.836
 
1.2%
11.935
 
1.0%

CHAS
Categorical

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.1 KiB
0
471 
1
 
35

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters506
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0471
93.1%
135
 
6.9%

Length

2023-04-10T15:55:43.978017image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-04-10T15:55:44.409041image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0471
93.1%
135
 
6.9%

Most occurring characters

ValueCountFrequency (%)
0471
93.1%
135
 
6.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number506
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0471
93.1%
135
 
6.9%

Most occurring scripts

ValueCountFrequency (%)
Common506
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0471
93.1%
135
 
6.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII506
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0471
93.1%
135
 
6.9%

NOX
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct81
Distinct (%)16.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5546950593
Minimum0.385
Maximum0.871
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2023-04-10T15:55:44.785971image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.385
5-th percentile0.40925
Q10.449
median0.538
Q30.624
95-th percentile0.74
Maximum0.871
Range0.486
Interquartile range (IQR)0.175

Descriptive statistics

Standard deviation0.1158776757
Coefficient of variation (CV)0.2089033853
Kurtosis-0.06466713337
Mean0.5546950593
Median Absolute Deviation (MAD)0.0875
Skewness0.7293079225
Sum280.6757
Variance0.01342763572
MonotonicityNot monotonic
2023-04-10T15:55:45.293483image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.53823
 
4.5%
0.71318
 
3.6%
0.43717
 
3.4%
0.87116
 
3.2%
0.62415
 
3.0%
0.48915
 
3.0%
0.69314
 
2.8%
0.60514
 
2.8%
0.7413
 
2.6%
0.54412
 
2.4%
Other values (71)349
69.0%
ValueCountFrequency (%)
0.3851
 
0.2%
0.3891
 
0.2%
0.3922
0.4%
0.3941
 
0.2%
0.3982
0.4%
0.44
0.8%
0.4013
0.6%
0.4033
0.6%
0.4043
0.6%
0.4053
0.6%
ValueCountFrequency (%)
0.87116
3.2%
0.778
1.6%
0.7413
2.6%
0.7186
 
1.2%
0.71318
3.6%
0.711
2.2%
0.69314
2.8%
0.6798
1.6%
0.6717
 
1.4%
0.6683
 
0.6%

RM
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct441
Distinct (%)88.0%
Missing5
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean6.284341317
Minimum3.561
Maximum8.78
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2023-04-10T15:55:45.797733image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum3.561
5-th percentile5.304
Q15.884
median6.208
Q36.625
95-th percentile7.61
Maximum8.78
Range5.219
Interquartile range (IQR)0.741

Descriptive statistics

Standard deviation0.7055867752
Coefficient of variation (CV)0.1122769658
Kurtosis1.858166444
Mean6.284341317
Median Absolute Deviation (MAD)0.349
Skewness0.4034215968
Sum3148.455
Variance0.4978526973
MonotonicityNot monotonic
2023-04-10T15:55:46.240320image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.2293
 
0.6%
6.4053
 
0.6%
5.7133
 
0.6%
6.1673
 
0.6%
6.1273
 
0.6%
6.4173
 
0.6%
5.392
 
0.4%
6.1852
 
0.4%
5.8882
 
0.4%
6.1932
 
0.4%
Other values (431)475
93.9%
(Missing)5
 
1.0%
ValueCountFrequency (%)
3.5611
0.2%
3.8631
0.2%
4.1382
0.4%
4.3681
0.2%
4.5191
0.2%
4.6281
0.2%
4.6521
0.2%
4.881
0.2%
4.9031
0.2%
4.9061
0.2%
ValueCountFrequency (%)
8.781
0.2%
8.7251
0.2%
8.7041
0.2%
8.3981
0.2%
8.3751
0.2%
8.3371
0.2%
8.2971
0.2%
8.2661
0.2%
8.2591
0.2%
8.2471
0.2%

AGE
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct356
Distinct (%)70.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean68.57490119
Minimum2.9
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2023-04-10T15:55:46.668866image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum2.9
5-th percentile17.725
Q145.025
median77.5
Q394.075
95-th percentile100
Maximum100
Range97.1
Interquartile range (IQR)49.05

Descriptive statistics

Standard deviation28.14886141
Coefficient of variation (CV)0.410483441
Kurtosis-0.9677155942
Mean68.57490119
Median Absolute Deviation (MAD)19.55
Skewness-0.5989626399
Sum34698.9
Variance792.3583985
MonotonicityNot monotonic
2023-04-10T15:55:47.065684image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10043
 
8.5%
95.44
 
0.8%
964
 
0.8%
98.24
 
0.8%
97.94
 
0.8%
98.84
 
0.8%
87.94
 
0.8%
95.63
 
0.6%
973
 
0.6%
21.43
 
0.6%
Other values (346)430
85.0%
ValueCountFrequency (%)
2.91
0.2%
61
0.2%
6.21
0.2%
6.51
0.2%
6.62
0.4%
6.81
0.2%
7.82
0.4%
8.41
0.2%
8.91
0.2%
9.81
0.2%
ValueCountFrequency (%)
10043
8.5%
99.31
 
0.2%
99.11
 
0.2%
98.93
 
0.6%
98.84
 
0.8%
98.71
 
0.2%
98.51
 
0.2%
98.42
 
0.4%
98.32
 
0.4%
98.24
 
0.8%

DIS
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct412
Distinct (%)81.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.795042688
Minimum1.1296
Maximum12.1265
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2023-04-10T15:55:47.350364image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1.1296
5-th percentile1.461975
Q12.100175
median3.20745
Q35.188425
95-th percentile7.8278
Maximum12.1265
Range10.9969
Interquartile range (IQR)3.08825

Descriptive statistics

Standard deviation2.105710127
Coefficient of variation (CV)0.5548580872
Kurtosis0.4879411222
Mean3.795042688
Median Absolute Deviation (MAD)1.29115
Skewness1.011780579
Sum1920.2916
Variance4.434015137
MonotonicityNot monotonic
2023-04-10T15:55:47.661975image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.49525
 
1.0%
5.72094
 
0.8%
5.28734
 
0.8%
6.81474
 
0.8%
5.40074
 
0.8%
6.33613
 
0.6%
3.94543
 
0.6%
6.4983
 
0.6%
4.72113
 
0.6%
4.81223
 
0.6%
Other values (402)470
92.9%
ValueCountFrequency (%)
1.12961
0.2%
1.1371
0.2%
1.16911
0.2%
1.17421
0.2%
1.17811
0.2%
1.20241
0.2%
1.28521
0.2%
1.31631
0.2%
1.32161
0.2%
1.33251
0.2%
ValueCountFrequency (%)
12.12651
0.2%
10.71032
0.4%
10.58572
0.4%
9.22291
0.2%
9.22032
0.4%
9.18761
0.2%
9.08921
0.2%
8.90672
0.4%
8.79212
0.4%
8.69661
0.2%

RAD
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct9
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.549407115
Minimum1
Maximum24
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2023-04-10T15:55:47.900944image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q14
median5
Q324
95-th percentile24
Maximum24
Range23
Interquartile range (IQR)20

Descriptive statistics

Standard deviation8.707259384
Coefficient of variation (CV)0.9118115166
Kurtosis-0.8672319936
Mean9.549407115
Median Absolute Deviation (MAD)2
Skewness1.004814648
Sum4832
Variance75.81636598
MonotonicityNot monotonic
2023-04-10T15:55:48.120068image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
24132
26.1%
5115
22.7%
4110
21.7%
338
 
7.5%
626
 
5.1%
224
 
4.7%
824
 
4.7%
120
 
4.0%
717
 
3.4%
ValueCountFrequency (%)
120
 
4.0%
224
 
4.7%
338
 
7.5%
4110
21.7%
5115
22.7%
626
 
5.1%
717
 
3.4%
824
 
4.7%
24132
26.1%
ValueCountFrequency (%)
24132
26.1%
824
 
4.7%
717
 
3.4%
626
 
5.1%
5115
22.7%
4110
21.7%
338
 
7.5%
224
 
4.7%
120
 
4.0%

TAX
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct66
Distinct (%)13.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean408.2371542
Minimum187
Maximum711
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2023-04-10T15:55:48.366648image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum187
5-th percentile222
Q1279
median330
Q3666
95-th percentile666
Maximum711
Range524
Interquartile range (IQR)387

Descriptive statistics

Standard deviation168.5371161
Coefficient of variation (CV)0.4128411987
Kurtosis-1.142407992
Mean408.2371542
Median Absolute Deviation (MAD)73
Skewness0.6699559418
Sum206568
Variance28404.75949
MonotonicityNot monotonic
2023-04-10T15:55:48.666026image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
666132
26.1%
30740
 
7.9%
40330
 
5.9%
43715
 
3.0%
30414
 
2.8%
26412
 
2.4%
39812
 
2.4%
38411
 
2.2%
27711
 
2.2%
22410
 
2.0%
Other values (56)219
43.3%
ValueCountFrequency (%)
1871
 
0.2%
1887
1.4%
1938
1.6%
1981
 
0.2%
2165
1.0%
2227
1.4%
2235
1.0%
22410
2.0%
2261
 
0.2%
2339
1.8%
ValueCountFrequency (%)
7115
 
1.0%
666132
26.1%
4691
 
0.2%
43715
 
3.0%
4329
 
1.8%
4303
 
0.6%
4221
 
0.2%
4112
 
0.4%
40330
 
5.9%
4022
 
0.4%

PTRATIO
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct46
Distinct (%)9.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.4555336
Minimum12.6
Maximum22
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2023-04-10T15:55:48.956744image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum12.6
5-th percentile14.7
Q117.4
median19.05
Q320.2
95-th percentile21
Maximum22
Range9.4
Interquartile range (IQR)2.8

Descriptive statistics

Standard deviation2.164945524
Coefficient of variation (CV)0.1173060379
Kurtosis-0.2850913833
Mean18.4555336
Median Absolute Deviation (MAD)1.15
Skewness-0.8023249269
Sum9338.5
Variance4.686989121
MonotonicityNot monotonic
2023-04-10T15:55:49.239829image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
20.2140
27.7%
14.734
 
6.7%
2127
 
5.3%
17.823
 
4.5%
19.219
 
3.8%
17.418
 
3.6%
18.617
 
3.4%
19.117
 
3.4%
18.416
 
3.2%
16.616
 
3.2%
Other values (36)179
35.4%
ValueCountFrequency (%)
12.63
 
0.6%
1312
 
2.4%
13.61
 
0.2%
14.41
 
0.2%
14.734
6.7%
14.83
 
0.6%
14.94
 
0.8%
15.11
 
0.2%
15.213
 
2.6%
15.33
 
0.6%
ValueCountFrequency (%)
222
 
0.4%
21.215
 
3.0%
21.11
 
0.2%
2127
 
5.3%
20.911
 
2.2%
20.2140
27.7%
20.15
 
1.0%
19.78
 
1.6%
19.68
 
1.6%
19.219
 
3.8%

B
Real number (ℝ≥0)

HIGH CORRELATION

Distinct357
Distinct (%)70.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean356.6740316
Minimum0.32
Maximum396.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2023-04-10T15:55:49.527238image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.32
5-th percentile84.59
Q1375.3775
median391.44
Q3396.225
95-th percentile396.9
Maximum396.9
Range396.58
Interquartile range (IQR)20.8475

Descriptive statistics

Standard deviation91.29486438
Coefficient of variation (CV)0.255961624
Kurtosis7.226817549
Mean356.6740316
Median Absolute Deviation (MAD)5.46
Skewness-2.890373712
Sum180477.06
Variance8334.752263
MonotonicityNot monotonic
2023-04-10T15:55:49.828289image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
396.9121
 
23.9%
393.743
 
0.6%
395.243
 
0.6%
376.142
 
0.4%
394.722
 
0.4%
395.632
 
0.4%
392.82
 
0.4%
395.562
 
0.4%
390.942
 
0.4%
393.682
 
0.4%
Other values (347)365
72.1%
ValueCountFrequency (%)
0.321
0.2%
2.521
0.2%
2.61
0.2%
3.51
0.2%
3.651
0.2%
6.681
0.2%
7.681
0.2%
9.321
0.2%
10.481
0.2%
16.451
0.2%
ValueCountFrequency (%)
396.9121
23.9%
396.421
 
0.2%
396.331
 
0.2%
396.31
 
0.2%
396.281
 
0.2%
396.241
 
0.2%
396.231
 
0.2%
396.212
 
0.4%
396.141
 
0.2%
396.062
 
0.4%

LSTAT
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct455
Distinct (%)89.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.65306324
Minimum1.73
Maximum37.97
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2023-04-10T15:55:50.133950image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1.73
5-th percentile3.7075
Q16.95
median11.36
Q316.955
95-th percentile26.8075
Maximum37.97
Range36.24
Interquartile range (IQR)10.005

Descriptive statistics

Standard deviation7.141061511
Coefficient of variation (CV)0.5643741263
Kurtosis0.4932395174
Mean12.65306324
Median Absolute Deviation (MAD)4.795
Skewness0.9064600936
Sum6402.45
Variance50.99475951
MonotonicityNot monotonic
2023-04-10T15:55:50.429529image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.793
 
0.6%
14.13
 
0.6%
6.363
 
0.6%
18.133
 
0.6%
8.053
 
0.6%
5.292
 
0.4%
13.442
 
0.4%
7.442
 
0.4%
18.062
 
0.4%
5.492
 
0.4%
Other values (445)481
95.1%
ValueCountFrequency (%)
1.731
0.2%
1.921
0.2%
1.981
0.2%
2.471
0.2%
2.871
0.2%
2.881
0.2%
2.941
0.2%
2.961
0.2%
2.971
0.2%
2.981
0.2%
ValueCountFrequency (%)
37.971
0.2%
36.981
0.2%
34.771
0.2%
34.411
0.2%
34.371
0.2%
34.021
0.2%
31.991
0.2%
30.812
0.4%
30.631
0.2%
30.621
0.2%

MEDV
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct229
Distinct (%)45.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.53280632
Minimum5
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2023-04-10T15:55:50.755604image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile10.2
Q117.025
median21.2
Q325
95-th percentile43.4
Maximum50
Range45
Interquartile range (IQR)7.975

Descriptive statistics

Standard deviation9.197104087
Coefficient of variation (CV)0.408165053
Kurtosis1.495196944
Mean22.53280632
Median Absolute Deviation (MAD)4
Skewness1.108098408
Sum11401.6
Variance84.58672359
MonotonicityNot monotonic
2023-04-10T15:55:51.071121image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5016
 
3.2%
258
 
1.6%
227
 
1.4%
21.77
 
1.4%
23.17
 
1.4%
19.46
 
1.2%
20.66
 
1.2%
13.85
 
1.0%
21.45
 
1.0%
20.15
 
1.0%
Other values (219)434
85.8%
ValueCountFrequency (%)
52
0.4%
5.61
 
0.2%
6.31
 
0.2%
72
0.4%
7.23
0.6%
7.41
 
0.2%
7.51
 
0.2%
8.11
 
0.2%
8.32
0.4%
8.42
0.4%
ValueCountFrequency (%)
5016
3.2%
48.81
 
0.2%
48.51
 
0.2%
48.31
 
0.2%
46.71
 
0.2%
461
 
0.2%
45.41
 
0.2%
44.81
 
0.2%
441
 
0.2%
43.81
 
0.2%

Interactions

2023-04-10T15:55:36.457601image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:54:46.653370image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:54:50.825223image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:54:55.608476image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:54:59.954452image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:55:03.480253image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:55:07.827615image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:55:10.983105image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:55:14.378829image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:55:17.712773image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:55:21.819756image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:55:25.524146image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:55:28.644283image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:55:32.966614image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:55:36.668419image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:54:46.885172image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:54:51.136343image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:54:55.836096image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:55:00.185372image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:55:03.823438image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:55:08.053369image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:55:11.201724image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:55:14.619869image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:55:18.064955image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:55:22.039462image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:55:25.746347image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:55:28.867031image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:55:33.346902image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:55:36.872558image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:54:47.137857image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:54:52.729288image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:54:56.035336image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:55:00.399966image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:55:04.187378image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:55:08.275293image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:55:11.427341image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:55:14.839013image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:55:18.412106image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:55:22.248918image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:55:25.979744image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:55:29.117181image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:55:33.648446image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:55:37.089832image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:54:47.739564image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:54:53.080196image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:54:56.249976image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:55:00.616501image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:55:04.525270image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:55:08.528080image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:55:11.885196image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:55:15.043537image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:55:18.687926image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:55:22.462383image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:55:26.195240image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:55:29.348125image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:55:33.867038image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:55:37.313519image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:54:47.963059image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:54:53.387165image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:54:56.468196image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:55:00.850724image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:55:04.819879image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:55:08.745464image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:55:12.114364image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:55:15.256027image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:55:19.007460image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:55:22.680487image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:55:26.413524image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:55:29.583862image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:55:34.091327image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:55:37.527829image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:54:48.196309image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:54:53.606367image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:54:56.678764image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:55:01.098457image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:55:05.169202image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:55:08.946110image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:55:12.326894image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:55:15.462377image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:55:19.332099image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:55:22.916421image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:55:26.637622image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:55:29.823880image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:55:34.330993image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:55:37.740028image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:54:48.732957image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:54:53.854272image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:54:56.902562image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:55:01.529533image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:55:05.523602image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:55:09.172676image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:55:12.554342image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:55:15.691833image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:55:19.734891image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:55:23.172257image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:55:26.859788image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:55:30.076291image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:55:34.571475image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:55:37.960295image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:54:48.946867image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:54:54.058628image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:54:57.109757image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:55:01.766260image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:55:05.876826image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:55:09.417659image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:55:12.761626image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:55:15.900800image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:55:20.059010image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:55:23.398813image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:55:27.084559image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:55:30.316121image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:55:34.795493image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:55:38.195262image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:54:49.195438image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:54:54.271543image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:54:57.323076image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:55:02.022738image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:55:06.259994image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:55:09.656740image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:55:12.970646image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:55:16.111317image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:55:20.391959image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:55:23.629538image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:55:27.298364image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:55:30.660491image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:55:35.019962image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:55:38.440938image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:54:49.429187image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:54:54.483661image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:54:57.564548image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:55:02.251553image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:55:06.592464image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:55:09.868048image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:55:13.178747image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:55:16.321833image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:55:20.614816image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:55:24.360575image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:55:27.516428image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:55:31.045765image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:55:35.251379image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:55:38.663304image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:54:49.660456image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:54:54.710876image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:54:57.895677image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:55:02.473099image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:55:06.855268image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:55:10.085251image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:55:13.410363image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:55:16.533530image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:55:20.879428image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:55:24.618498image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:55:27.745982image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:55:31.435672image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:55:35.497100image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:55:38.887782image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:54:49.896379image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:54:54.914309image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:54:58.371543image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:55:02.695101image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:55:07.077636image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:55:10.292939image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:55:13.649203image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:55:16.756508image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:55:21.105864image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:55:24.828668image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:55:27.965680image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:55:31.787873image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:55:35.733758image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:55:39.162169image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:54:50.145572image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:54:55.148482image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:54:59.117589image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:55:02.932479image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:55:07.356350image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:55:10.535975image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:55:13.901251image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:55:17.063209image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:55:21.347994image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:55:25.069610image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:55:28.198691image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:55:32.197506image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:55:35.989599image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:55:39.789005image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:54:50.457592image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:54:55.378789image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:54:59.576913image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:55:03.184803image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:55:07.611188image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:55:10.775521image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:55:14.142171image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:55:17.348939image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:55:21.588926image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:55:25.295675image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:55:28.438665image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:55:32.597638image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-10T15:55:36.243955image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-04-10T15:55:51.334991image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2023-04-10T15:55:51.687133image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2023-04-10T15:55:52.035619image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2023-04-10T15:55:52.373090image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2023-04-10T15:55:40.162691image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-04-10T15:55:40.836412image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-04-10T15:55:41.072197image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

Unnamed: 0CRIMZNINDUSCHASNOXRMAGEDISRADTAXPTRATIOBLSTATMEDV
000.0063218.02.3100.5386.57565.24.0900129615.3396.904.9824.0
110.027310.07.0700.4696.42178.94.9671224217.8396.909.1421.6
220.027290.07.0700.4697.18561.14.9671224217.8392.834.0334.7
330.032370.02.1800.4586.99845.86.0622322218.7394.632.9433.4
440.069050.02.1800.4587.14754.26.0622322218.7396.905.3336.2
550.029850.02.1800.4586.43058.76.0622322218.7394.125.2128.7
660.0882912.57.8700.5246.01266.65.5605531115.2395.6012.4322.9
770.1445512.57.8700.5246.17296.15.9505531115.2396.9019.1527.1
880.2112412.57.8700.5245.631100.06.0821531115.2386.6329.9316.5
990.1700412.57.8700.5246.00485.96.5921531115.2386.7117.1018.9

Last rows

Unnamed: 0CRIMZNINDUSCHASNOXRMAGEDISRADTAXPTRATIOBLSTATMEDV
4964960.289600.09.6900.5855.39072.92.7986639119.2396.9021.1419.7
4974970.268380.09.6900.5855.79470.62.8927639119.2396.9014.1018.3
4984980.239120.09.6900.5856.01965.32.4091639119.2396.9012.9221.2
4994990.177830.09.6900.5855.56973.52.3999639119.2395.7715.1017.5
5005000.224380.09.6900.5856.02779.72.4982639119.2396.9014.3316.8
5015010.062630.011.9300.5736.59369.12.4786127321.0391.999.6722.4
5025020.045270.011.9300.5736.12076.72.2875127321.0396.909.0820.6
5035030.060760.011.9300.5736.97691.02.1675127321.0396.905.6423.9
5045040.109590.011.9300.5736.79489.32.3889127321.0393.456.4822.0
5055050.047410.011.9300.5736.03080.82.5050127321.0396.907.8811.9